MCP Clients

Description: A transparent desktop overlay that gives your AI agent a physical presence on screen. Your AI picks its own body from a library of avatars, colors, and expressions — you don't choose for it. Windows, $4.99, 7-day free trial, no account required.

MCP (Model Context Protocol) Server for Sychev Lab - provides access to products, articles, tutorials, and e-commerce features located in https://lab.sychve.xyz

A standalone Python/FastAPI server that implements the Model Context Protocol (MCP) for the OPTIX threat intelligence platform. It exposes 26 analyst-friendly tools that AI assistants and programmatic consumers can use to query threat feeds, search documents and indicators, manage watchlists, triage IOCs, generate detection rules, trigger AI research, and produce intelligence reports — without needing to understand OPTIX's internal REST API.

Your Data. Your AI. Your Rules. Ontheia is a self-hosted, open-source AI agent platform. Run AI agents, automate workflows, and connect AI models to any tool — entirely on your own infrastructure, without sending data to external cloud services.

GPT Image 2 AI is an MCP-compatible service for generating and transforming images using text prompts. It provides a simple interface for AI agents and developer tools to create visual outputs, refine images, and experiment with generative workflows in a programmatic way. The service is designed to be lightweight and easy to integrate into MCP-based environments, enabling use cases such as: text-to-image generation image refinement and variation visual prototyping workflows AI-assisted content pipelines 👉 https://gptimg2ai.net This server can be used by AI agents to generate visual assets dynamically, making it suitable for creative tooling, automation workflows, and experimental AI applications.

StationOne is a local-first, model-agnostic AI client and orchestration layer that enables users to build, run, and manage agentic workflows across multiple LLMs and MCP connectors. It provides a unified workspace for integrating AI models, tools, and data sources through the Model Context Protocol (MCP), allowing agents to securely interact with external services like APIs, databases, and SaaS platforms. With support for custom agents, pre-prompt templates, and a curated MCP marketplace, StationOne acts as a central hub for deploying scalable, context-aware AI automation across personal and enterprise environments.

Explorer-first MCP client that renders any MCP server's tools as interactive UI — no LLM required, no tool-calling loop. Point it at any MCP server over stdio or SSE and get cards, tables, charts, forms, pipelines, and dashboards auto-generated from your tool outputs. Install with `npx burnish -- <your-mcp-server>`.

## Overview ### what is Chain.Love MCP? Chain.Love MCP is a hosted remote MCP server and gateway for AI agents. It provides a single endpoint for discovering and comparing Web3 infrastructure services across 50+ blockchain networks, including RPCs, indexing, oracles, storage, compute, and developer tools. ### how to use Chain.Love MCP? To use Chain.Love MCP, add the hosted endpoint to your MCP client and connect to `https://app.chain.love/mcp` over Streamable HTTP. For public use cases, the basic MCP server URL is enough. For private downstream MCPs, add credentials only when required using `x-chainlove-cred-<credentialKey>` headers. ### key features of Chain.Love MCP? - Hosted remote MCP gateway for AI agents - Single endpoint for Web3 infrastructure discovery across 50+ blockchain networks - Aggregates infrastructure options across RPCs, indexing, oracles, storage, compute, and developer tools - Streamable HTTP transport - Public documentation and onboarding resources available online ### use cases of Chain.Love MCP? - Discovering and comparing Web3 infrastructure providers across many blockchain networks - Finding RPC, indexing, oracle, storage, compute, and developer tooling options through one MCP server - Giving AI agents a single hosted integration surface for Web3 infrastructure discovery - Reducing the need to integrate many separate provider-specific endpoints ### FAQ from Chain.Love MCP? - Can Chain.Love MCP be used as a hosted remote MCP server? Yes. Chain.Love MCP is designed to be consumed as a hosted remote MCP endpoint at `https://app.chain.love/mcp`. - Does Chain.Love MCP require credentials? Not always. Some downstream integrations may require credentials, which can be passed using `x-chainlove-cred-<credentialKey>` headers when needed. - How do I know which credential header to use? You can check the open-source Chain.Love registry at `https://github.com/Chain-Love/chain-love/blob/main/references/offers/mcpservers.csv` or browse `https://app.chain.love/toolbox/mcpservers` and look for the relevant `credentialKey` value. - Where can I learn more? Landing page: `https://www.chain.love/mcp-gateway` Documentation: `https://chain-love.gitbook.io/mcp-module`

Community Internal MCP server for Airtable - 30 tools for schema inspection, field CRUD (formula, rollup, lookup, count), view configuration, formula validation, and extension management. Not affiliated with Airtable Inc.

Studio is where you run MCP tools in the browser against real credentials -- see inputs, outputs, and errors before you trust an agent. Enable bundles in your workspace, then add your Hub or per-bundle streamable HTTP URL in Cursor, Claude, ChatGPT, or VS Code. Workspace-scoped credentials; optional mcpbundles CLI on PyPI.

Rust MCP server that gives AI agents reliable web access. Bypasses Cloudflare and bot protection using Chrome-level TLS fingerprinting, no headless browser needed. 10 tools for scraping, crawling, search, structured extraction, summarization, and more. Output is clean markdown optimized for LLMs with 67% fewer tokens than raw HTML. 8 of 10 tools work locally without an API key.

MCPMate is a comprehensive Model Context Protocol (MCP) management center designed to address configuration complexity, resource consumption, security risks, and other issues in the MCP ecosystem, providing users with a unified management platform.

Manage your LinkaGoGo bookmarks through any AI assistant that supports the Model Context Protocol (MCP). Search, add, organize, tag, move, and export bookmarks conversationally — 16 tools for full bookmark and folder management. Connect via Claude.ai, Claude Desktop, or any MCP-compatible client.

AiPy Pro 就是个能听懂你说话的智能助手，跟它说句话就能帮你写好 PPT、自动操作电脑、手机和服务器，还能处理数据写代码，不用自己动手，工作省事很多。

MCPwner is a Model Context Protocol (MCP) server that integrates security testing tools into LLM-driven workflows. It provides a unified interface for secret scanning, static analysis (SAST), software composition analysis (SCA), and vulnerability research including 0-day discovery. Instead of manually chaining tools and pasting outputs into your LLM, MCPwner standardizes and streams results directly into the model's working context. This enables continuous reasoning, correlation, and attack path discovery across the entire security research lifecycle - from identifying known vulnerabilities to uncovering novel attack vectors.

Image to Image AI is an AI image and video platform that lets you transform reference images or generate new ones from text. Upload one or several images, add a prompt, and get high-quality outputs in multiple aspect ratios (1:1, 16:9, 9:16, etc.) and resolutions (1K, 2K, 4K). It runs on AI Best and supports 9+ models including Nano Banana, Nano Banana Pro, GPT-4o Image, Flux Kontext, and video models like Veo. Use it for product shots, social content, concept art, or marketing—with sharper 2K imagery, 4K scaling, better text in images, and consistent characters.